Senior AI Platform Architect

Key Skills

Aartificial intelligence, AWS, Azure, Continuous Integration & Continuous Delivery - CI/CD, Deep Learning, DevOps, Effective communication skills, Large Language Models - LLMs, Machine learning techniques, MLOps tools, PyTorch, TensorFlow

Job Description

Responsibilities

• As a Senior AI Platform Architect, you are responsible for architecting, building, and optimizing the core technologies and infrastructure to enable and scale AI applications through the enterprise.
• Lead development of the AI/ML platform, focusing on modern, scalable, and robust design principles.
• Collaborate with cross-functional teams, such as AI, data, and software engineers, infrastructure teams, and business units to enable effective AI delivery that meets ADI’s strategic goals.
• Provide technical leadership in AI system design, overseeing the entire AI/ML lifecycle.
• Implement and uphold modern software design principles, ensuring best practices in CI/CD, MLOps, and infrastructure-as-code.
• Manage and optimize the AI platform’s stability, scalability, and performance.
• Proactively ensure the security and integrity of the AI platform, data, and models, and take part in security and privacy design reviews.
• Conduct research to stay ahead of the latest developments in AI, communicate learnings to the team, and evaluate / recommend new technologies for the platform.

Qualifications

• 10+ years of experience in software engineering, including 5+ years working on AI delivery platforms and 5+ years in a software leadership role.
• Degree in Computer Science, Electrical Engineering, Computer Engineering, or a related field.
• Broad expertise and experience with ML, deep learning, LLMs, and modern AI trends.
• Demonstrated experience in modern AI/ML technologies and proven ability to architect and build AI pipelines, platforms, or similar infrastructure at enterprise scale.
• Experience architecting distributed systems and cloud infrastructure, and using modern deployment technologies, such as Kubernetes.
• Expertise with relevant languages and technologies such as Python, C++, Pytorch, and Tensorflow. Experience with Azure or AWS.
• Proven ability to integrate with data engineering platforms and utilize/build modern DevOps / MLOps processes.
• A confident peer influencer with strong communication skills who quickly establishes credibility and is capable of leading cross-functional technical teams.